Bayes Factors for Mixed Models

Abstract Although Bayesian linear mixed effects models are increasingly popular for analysis of within-subject designs in psychology and other fields, there remains considerable ambiguity on the most appropriate Bayes factor hypothesis test to quantify degree which data support presence or absence an experimental effect. Specifically, different choices both null model alternative possible, each choice constitutes a definition effect resulting outcome. We outline common approaches focus impact aggregation, measurement error, prior distribution, detection interactions. For concreteness, three example scenarios showcase how seemingly innocuous can lead dramatic differences statistical evidence. hope this work will facilitate more explicit discussion about best practices testing models.